Associating features with entities, such as categories of web page documents, and/or weighting such features

ABSTRACT

Features that may be used to represent relevance information (e.g., properties, characteristics, etc.) of an entity, such as a document or concept for example, may be associated with the document by accepting an identifier that identifies a document; obtaining search query information (and/or other serving parameter information) related to the document using the document identifier, determining features using the obtained query information (and/or other serving parameter information), and associating the features determined with the document. Weights of such features may be similarly determined. The weights may be determined using scores. The scores may be a function of one or more of whether the document was selected, a user dwell time on a selected document, whether or not a conversion occurred with respect to the document, etc. The document may be a Web page. The features may be n-grams. The relevance information of the document may be used to target the serving of advertisements with the document.

§ 1. BACKGROUND OF THE INVENTION

§ 1.1 Field of the Invention

The present invention concerns advertising. In particular, the presentinvention concerns improving targeted advertising.

§ 1.2 Background Information

Interactive advertising provides opportunities for advertisers to targettheir ads to a receptive audience. That is, targeted ads are more likelyto be useful to end users since the ads may be relevant to a needinferred from some user activity (e.g., relevant to a user's searchquery to a search engine, relevant to content in a document requested bythe user, etc.). Query keyword relevant advertising has been used bysearch engines. The AdWords advertising system by Google of MountainView, Calif. is one example of query keyword relevant advertising.Similarly, content-relevant advertising systems, such as the AdSenseadvertising system by Google for example, have been used. For example,U.S. patent application Ser. No. 10/314,427 (incorporated herein byreference and referred to as “the '427 application”) titled “METHODS ANDAPPARATUS FOR SERVING RELEVANT ADVERTISEMENTS”, filed on Dec. 6, 2002and listing Jeffrey A. Dean, Georges R. Harik and Paul Bucheit asinventors, and Ser. No. 10/375,900 (incorporated by reference andreferred to as “the '900 application”) titled “SERVING ADVERTISEMENTSBASED ON CONTENT,” filed on Feb. 26, 2003 and listing Darrell Anderson,Paul Bucheit, Alex Carobus, Claire Cui, Jeffrey A. Dean, Georges R.Harik, Deepak Jindal and Narayanan Shivakumar as inventors, describemethods and apparatus for serving ads relevant to the content of adocument, such as a Web page for example.

When ads are to be served using some measure of their relevance todocument, relevance information about the document is needed. Suchrelevance information may be determined from information intrinsic tothe document, such as content extracted from the document. For example,concepts or topics may be determined using the content of the document.The document may also be assigned to one or more clusters. (See, e.g.,U.S. Provisional Application Ser. No. 60/416,144 (incorporated herein byreference), titled “METHODS AND APPARATUS FOR PROBALISTIC HIERARCHICALINFERENTIAL LEARNER,” filed on Oct. 3, 2003 In another example, featurevectors may be used to represent the occurrence of words and/or phrasesin the document. Although such techniques for determining relevanceinformation for documents have worked well, it is desirable to be ableto provide additional relevance information, and/or to refine therelevance information to make it more useful.

Further if ads are to be associated with categories (e.g., for targetingto document categories, for association with categorical listings, etc.)it would be useful to develop and/or test such associations. Similarly,if query terms are to be associated with categories (e.g., forgenerating a categorized result page in response to a search query), itwould be useful to develop and/or test such associations.

In view of the foregoing, it would be useful to expand and/or refinedocument and/or category relevance information. More generally, it wouldbe useful to associate features with entities, such as documents,categories, etc. It would also be useful to score (e.g., weight) suchassociations.

§ 2. SUMMARY OF THE INVENTION

Embodiments consistent with the present invention may be used todetermine features that may be used to represent relevance information(e.g., properties, characteristics, etc.) of an entity, such as adocument or category for example. Such features may be determined andassociated with the entity by accepting an identifier that identifiesthe entity, obtaining search query information related to the entityusing the entity identifier, determining features using the obtainedquery information, and associating the features determined with theentity. In at least some embodiments consistent with the presentinvention, such features may be determined for an entity using queryinformation, and/or perhaps user action information. In at least someembodiments consistent with the present invention, in addition to, orinstead of, query information, other serving parameter information maybe used to determine and/or weight features.

In at least some embodiments consistent with the present invention,weights of such features may be similarly determined. The weights may bedetermined using scores. In the context of document entities, the scoresmay be a function of one or more of (i) whether the document wasselected, (ii) a user dwell time on a selected document, (iii) whetheror not a conversion occurred with respect to the document, (iv) afrequency of queries including the feature, etc.

In at least some embodiments consistent with the present invention, thedocument is a Web page. In at least some embodiments consistent with thepresent invention, the features are n-grams.

In at least some embodiments consistent with the present invention, therelevance information of the document may be used to target the servingof advertisements with the document. In at lease some other embodimentsconsistent with the present invention, the features of a category may beused to associate query terms and categories, and/or ads and categories.

In at least some embodiments consistent with the present invention, ascore (e.g., a weight) associated with the feature-to-entity associationmay be updated by (i) using the feature-to-entity association togenerate one or more results for presentation to a user, (ii) trackinguser behavior with respect to the results, and (ii) updating the scoreassociated with the feature-to-entity association using the tracked userbehavior.

§ 3. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary on-line advertisingenvironment in which, or with which, the present invention may be used.

FIG. 2 is a bubble diagram illustrating operations that may beperformed, and information that may be generated, used, and/or stored,by a document feature generation and/or update system consistent withthe present invention.

FIG. 3 is a bubble chart illustrating operations that may be used withsearch operations to associate query terms and selections with documentsin a manner consistent with the present invention.

FIG. 4 is a bubble diagram illustrating operations that may beperformed, and information that may be generated, used, and/or stored,by a document feature generation and/or update system consistent withthe present invention.

FIG. 5 is a flow diagram of an exemplary method that may be used togenerate and/or update document feature information in a mannerconsistent with the present invention.

FIG. 6 is a flow diagram of an exemplary method that may be used togenerate and/or update document feature information in a mannerconsistent with the present invention.

FIG. 7 is block diagram of a machine that may perform one or moreoperations and store information used and/or generated in a mannerconsistent with the present invention.

FIG. 8 is a diagram illustrating an example of how an exemplaryembodiment consistent with present invention can make associationsbetween categories and query terms and/or ads.

§ 4. DETAILED DESCRIPTION

The present invention may involve novel methods, apparatus, messageformats, and/or data structures for associating one or more featureswith an entity, such as a Web page document, or category for example,and/or applying and/or adjusting a score or weight to at least one ofsuch features. The following description is presented to enable oneskilled in the art to make and use the invention, and is provided in thecontext of particular applications and their requirements. Thus, thefollowing description of embodiments consistent with the presentinvention provides illustration and description, but is not intended tobe exhaustive or to limit the present invention to the precise formdisclosed. Various modifications to the disclosed embodiments will beapparent to those skilled in the art, and the general principles setforth below may be applied to other embodiments and applications. Forexample, although a series of acts may be described with reference to aflow diagram, the order of acts may differ in other implementations whenthe performance of one act is not dependent on the completion of anotheract. Further, non-dependent acts may be performed in parallel. Noelement, act or instruction used in the description should be construedas critical or essential to the present invention unless explicitlydescribed as such. Also, as used herein, the article “a” is intended toinclude one or more items. Where only one item is intended, the term“one” or similar language is used. Thus, the present invention is notintended to be limited to the embodiments shown and the inventors regardtheir invention as any patentable subject matter described.

In the following, definitions of terms that may be used in thespecification are provided in § 4.1. Then, environments in which, orwith which, the present invention may operate are described in § 4.2.Thereafter, exemplary embodiments consistent with the present inventionare described in § 4.3. An example illustrating an operation in anexemplary embodiment consistent with the present invention is providedin §4.4. Finally, some conclusions regarding the present invention areset forth in § 4.5.

§ 4.1. Definitions

Online ads may have various intrinsic features. Such features may bespecified by an application and/or an advertiser. These features arereferred to as “ad features” below. For example, in the case of a textad, ad features may include a title line, ad text, and an embedded link.In the case of an image ad, ad features may include images, executablecode, and an embedded link. Depending on the type of online ad, adfeatures may include one or more of the following: text, a link, anaudio file, a video file, an image file, executable code, embeddedinformation, etc.

When an online ad is served, one or more parameters may be used todescribe how, when, and/or where the ad was served. These parameters arereferred to as “serving parameters” below. Serving parameters mayinclude, for example, one or more of the following: features of(including information on) a document on which, or with which, the adwas served, a search query or search results associated with the servingof the ad, a user characteristic (e.g., their geographic location, thelanguage used by the user, the type of browser used, previous pageviews, previous behavior), a host or affiliate site (e.g., AmericaOnline, Google, Yahoo) that initiated the request, an absolute positionof the ad on the page on which it was served, a position (spatial ortemporal) of the ad relative to other ads served, an absolute size ofthe ad, a size of the ad relative to other ads, a color of the ad, anumber of other ads served, types of other ads served, time of dayserved, time of week served, time of year served, etc. Naturally, thereare other serving parameters that may be used in the context of theinvention.

Although serving parameters may be extrinsic to ad features, they may beassociated with an ad as serving conditions or constraints. When used asserving conditions or constraints, such serving parameters are referredto simply as “serving constraints” (or “targeting criteria”). Forexample, in some systems, an advertiser may be able to target theserving of its ad by specifying that it is only to be served onweekdays, no lower than a certain position, only to users in a certainlocation, etc. As another example, in some systems, an advertiser mayspecify that its ad is to be served only if a page or search queryincludes certain keywords or phrases. As yet another example, in somesystems, an advertiser may specify that its ad is to be served only if adocument being served includes certain topics or concepts, or fallsunder a particular cluster or clusters, or some other classification orclassifications.

“Ad information” may include any combination of ad features, ad servingconstraints, information derivable from ad features or ad servingconstraints (referred to as “ad derived information”), and/orinformation related to the ad (referred to as “ad related information”),as well as an extension of such information (e.g., information derivedfrom ad related information).

The ratio of the number of selections (e.g., clickthroughs) of an ad tothe number of impressions of the ad (i.e., the number of times an ad isrendered) is defined as the “selection rate” (or “clickthrough rate”) ofthe ad.

A “conversion” is said to occur when a user consummates a transactionrelated to a previously served ad. What constitutes a conversion mayvary from case to case and can be determined in a variety of ways. Forexample, it may be the case that a conversion occurs when a user clickson an ad, is referred to the advertiser's Website, and consummates apurchase there before leaving that Website. Alternatively, a conversionmay be defined as a user being shown an ad, and making a purchase on theadvertiser's Website within a predetermined time (e.g., seven days). Inyet another alternative, a conversion may be defined by an advertiser tobe any measurable/observable user action such as, for example,downloading a white paper, navigating to at least a given depth of aWebsite, viewing at least a certain number of Web pages, spending atleast a predetermined amount of time on a Website or Web page,registering on a Website, etc. Often, if user actions don't indicate aconsummated purchase, they may indicate a sales lead, although useractions constituting a conversion are not limited to this. Indeed, manyother definitions of what constitutes a conversion are possible.

The ratio of the number of conversions to the number of impressions ofthe ad (i.e., the number of times an ad is rendered) is referred to asthe “conversion rate.” If a conversion is defined to be able to occurwithin a predetermined time since the serving of an ad, one possibledefinition of the conversion rate might only consider ads that have beenserved more than the predetermined time in the past.

A “document” is to be broadly interpreted to include anymachine-readable and machine-storable work product. A document may be afile, a combination of files, one or more files with embedded links toother files, etc. The files may be of any type, such as text, audio,image, video, etc. Parts of a document to be rendered to an end user canbe thought of as “content” of the document. A document may include“structured data” containing both content (words, pictures, etc.) andsome indication of the meaning of that content (for example, e-mailfields and associated data, HTML tags and associated data, etc.) Adspots in the document may be defined by embedded information orinstructions. In the context of the Internet, a common document is a Webpage. Web pages often include content and may include embeddedinformation (such as meta information, hyperlinks, etc.) and/or embeddedinstructions (such as JavaScript, etc.). In many cases, a document has aunique, addressable, storage location and can therefore be uniquelyidentified by this addressable location. A universal resource locator(URL) is a unique address used to access information on the Internet.

“Document information” may include any information included in thedocument, information derivable from information included in thedocument (referred to as “document derived information”), and/orinformation related to the document (referred to as “document relatedinformation”), as well as an extensions of such information (e.g.,information derived from related information). An example of documentderived information is a classification based on textual content of adocument. Examples of document related information include documentinformation from other document(s) with links to the instant document,as well as document information from other document(s) to which theinstant document links and document information from other document(s)related to the instant document.

Content from a document may be rendered on a “content renderingapplication or device”. Examples of content rendering applications ordevices include an Internet browser (e.g., Explorer or Netscape), amedia player (e.g., an MP3 player, a Realnetworks streaming audio fileplayer, etc.), a viewer (e.g., an Abobe Acrobat pdf reader), etc.

A “content owner” is a person or entity that has some property right inthe content of a document. A content owner may be an author of thecontent. In addition, or alternatively, a content owner may have rightsto reproduce the content, rights to prepare derivative works of thecontent, rights to display or perform the content publicly, and/or otherproscribed rights in the content. Although a content server might be acontent owner in the content of the documents it serves, this is notnecessary.

“User information” may include user behavior information and/or userprofile information.

“E-mail information” may include any information included in an e-mail(also referred to as “internal e-mail information”), informationderivable from information included in the e-mail and/or informationrelated to the e-mail, as well as extensions of such information (e.g.,information derived from related information). An example of informationderived from e-mail information is information extracted or otherwisederived from search results returned in response to a search querycomposed of terms extracted from an e-mail subject line. Examples ofinformation related to e-mail information include e-mail informationabout one or more other e-mails sent by the same sender of a givene-mail, or user information about an e-mail recipient. Informationderived from or related to e-mail information may be referred to as“external e-mail information.”

§ 4.2 Environments In Which, or With Which, the Present Invention MayOperate

FIG. 1 illustrates an exemplary environment 100 in which, or with which,the present invention may be used. A user device (also referred to as a“client” or “client device”) 150 may include a browser facility (such asthe Explorer browser from Microsoft, the Opera Web Browser from OperaSoftware of Norway, the Navigator browser from AOL/Time Warner, etc.),an e-mail facility (e.g., Outlook from Microsoft), or any other softwareapplication or hardware device used to render content. A search engine120 may permit user devices 150 to search collections of documents(e.g., Web pages). A content server 130 may permit user devices 150 toaccess (e.g., for rendering) documents. An e-mail server (such asHotmail from Microsoft Network, Yahoo Mail, GMail from Google, etc.) 140may be used to provide e-mail functionality to user devices 150. An adserver 110 may be used to serve ads to user devices 150. The ads may beserved in association with search results provided by the search engine120. Content-relevant ads may be served in association with contentprovided by the content server 130, and/or e-mail supported by thee-mail server 140 and/or user device 150 e-mail facilities. Thus, the adserver 110 may be a content-relevant ad server, such as those describedin the '427 and '900 applications introduced above.

As discussed in the '900 application (introduced above), ads may betargeted to documents served by content servers. Thus, a content server130 that receives requests for documents (e.g., articles, discussionthreads, music, video, graphics, search results, Web page listings,etc.), and retrieves the requested document in response to, or otherwiseservices, the request may consume ads. The content server 130 may submita request for ads to the ad server 110. Alternatively, or in addition, auser device 150 may submit such a request. Alternatively, or inaddition, a Web-based e-mail server 140 may submit such a request. Suchan ad request may include a number of ads desired. The ad request mayalso include document request information. This information may includethe document itself (e.g., a Web page), a category or topiccorresponding to the content of the document or the document request(e.g., arts, business, computers, arts-movies, arts-music, etc.), partor all of the document request, content age, content type (e.g., text,graphics, video, audio, mixed media, etc.), geolocation information, enduser local time information, document information (such as documentfeatures for example), etc.

The content server 130, Web-based e-mail server 140, and/or user device150 may combine the requested document with one or more of theadvertisements provided by the ad server 110. This combined informationincluding the document content and advertisement(s) is then forwardedtowards, and/or rendered on, the end user device 150 that requested thedocument, for presentation to the user. Alternatively, or in addition,the ad(s) may be combined with, or rendered with, the requested documentin some other way (e.g., by the client device). Finally, the contentserver 130 or Web-based e-mail server 140 may transmit information aboutthe ads and how, when, and/or where the ads are to be rendered (e.g.,position, clickthrough or not, impression time, impression date, size,conversion or not, etc.) back to the ad server 110. Alternatively, or inaddition, such information may be provided back to the ad server 110 bysome other means. Consistent with the present invention, the ad server110 may store ad performance information.

A search engine 120 may receive queries for search results and mayconsume ads. In response, the search engine may retrieve relevant searchresults (e.g., from an index of Web pages). An exemplary search engineis described in the article S. Brin and L. Page, “The Anatomy of aLarge-Scale Hypertextual Search Engine,” Seventh International WorldWide Web Conference, Brisbane, Australia and in U.S. Pat. No. 6,285,999(both incorporated herein by reference). Such search results mayinclude, for example, lists of Web page titles, snippets of textextracted from those Web pages, and hypertext links to those Web pages,and may be grouped into a predetermined number of (e.g., ten) searchresults.

The search engine 120 may submit a request for ads to the ad server 110.The request may include a number of ads desired. This number may dependon the search results, the amount of screen or page space occupied bythe search results, the size and shape of the ads, etc. In oneembodiment, the number of desired ads will be from one to ten, andpreferably from three to five. The request for ads may also include thequery (as entered or parsed), information based on the query (such asend user local time information, geolocation information, whether thequery came from an affiliate and an identifier of such an affiliate),and/or information associated with, or based on, the search results.Such information may include, for example, identifiers related to thesearch results (e.g., document identifiers or “docIDs”), scores relatedto the search results (e.g., information retrieval (“IR”) scores such asdot products of feature vectors corresponding to a query and a document,Page Rank scores, and/or combinations of IR scores and Page Rankscores), snippets of text extracted from identified documents (e.g., Webpages), full text of identified documents, topics of identifieddocuments, feature vectors of identified documents, etc.

The search engine 120 may combine the search results with one or more ofthe advertisements provided by the ad server 110. Alternatively, or inaddition, the ad(s) may be combined with, or rendered with, therequested document in some other way (e.g., by the client device). Thiscombined information including the search results and advertisement(s)is then forwarded towards the user that submitted the search, forpresentation to the user. Preferably, the search results are maintainedas distinct from the ads, so as not to confuse the user between paidadvertisements and presumably neutral search results.

Finally, the search engine 120 may transmit information about the ad andwhen (e.g., end user local time), where (e.g., geolocation), and/or howthe ad was to be rendered (e.g., position, click-through or not,impression time, impression date, size, conversion or not, etc.) back tothe ad server 110. Alternatively, or in addition, such information maybe provided back to the ad server 110 by some other means. Consistentwith the present invention, the search engine 120 may also associatesearch query information (and/or other serving parameter information)with the documents associated with search results, documents associatedwith ads, and/or ads. The search engine 120 may also associate thesearch query information with user actions (e.g., selections, dwelltime, etc.) with respect to the documents linked from the search resultpages, and/or user actions (e.g., selections, conversions, etc.) withrespect to the ads rendered with the search results pages.

The Web-based e-mail server 140 may be thought of, generally, as acontent server in which a document served is simply an e-mail. Further,e-mail applications (such as Microsoft Outlook for example) may be usedto send and/or receive e-mail. Therefore, a Web-based e-mail server 140or a client device 150 application may be thought of as an ad consumer.Thus, e-mails may be thought of as documents, and targeted ads may beserved in association with such documents. For example, one or more adsmay be served in, under, over, or otherwise in association with ane-mail.

The various servers may exchange information via one or more networks160, such as the Internet for example.

§ 4.3 Exemplary Embodiments § 4.3.1 Overview

The present invention permits features, such as keywords or topics, tobe associated with entities, such as Web pages or categories.(Generally, entities (or representatives of entities) can be put on aresult page, and can be acted on by users.) Such associations may beused for a variety or reasons, such as, for example, targeting ads,suggesting targeting features for an advertisement for presentation toadvertisers, automatically generating targeting criteria for anadvertisement, etc. In some embodiments consistent with the presentinvention, features are associated with entities using search enginequery logs, search engine referrals, and/or other user actions withrespect to documents associated with a search results page. Methods andapparatus consistent with the present invention can improve theeffectiveness of marketing campaigns, and can reduce the amount of work(and cost) in running a campaign.

FIG. 2 is a bubble diagram illustrating operations 235 that may beperformed, and information that may be generated, used, and/or stored,by a document feature generation and/or update system consistent withthe present invention, as well as operations for generating informationused by such operations 235. As shown, operations 235 may accept adocument identifier (such as, for example, a URL if the document is aWeb page) 220, use the document identifier 220 to obtain query (and/oruser action) information 210 associated with the document, and generateand/or update features (and perhaps weights) for the document 260 usingthe obtained query (and/or user action) information. More specifically,document query information lookup operations 230 may use the documentidentifier 220 to lookup query (and/or user action) information 240pertaining to the identified document 220 from stored information 210.Document feature (vector) generation/update operations 250 may then usethis query (and/or user action) information 240 to generate features(and perhaps weights) 260 associated with the identified document.

In the foregoing example, it was assumed that the document identifier toquery (and/or user action) information association information 210 wasavailable. This information may have been generated by the operationsillustrated above the dashed line 299. For example, query (and/or useraction) logging operations 270 may be used to generate an aggregated logof query to document associations, and perhaps user action (includinginaction) to document associations 280. Index inverting operations 290may be used to generate the document identifier to query (and/or useraction) information associations 210 from the aggregated log of query todocument associations, and perhaps user action (including inaction) todocument associations 280.

FIG. 3 is a bubble chart illustrating operations that may be used withsearch operations to associate query terms and selections with documentsin a manner consistent with the present invention. In response to asearch query 320, search operations 310 use term to document invertedindex information 340 and perhaps search ranking information 350 togenerate a search results document 330. The document 330 may include oneor more search results 360. The document 330 may also include one ormore ads 370. The search results 360 and/or ads 370 may be selected asindicated by cursor click 380. Referring back to FIG. 2, query (and/oruser action) logging operations 270 may be used to log associationsbetween query information 320 and document identifiers (such as URLs orad identifiers for example) corresponding to the search results 360and/or ads 370. These operations 270 may also be used to logassociations between user actions (e.g., selections, conversions, dwelltime, etc.) and document identifiers (such as URLs or ad identifiers forexample) corresponding to the search results 360 and/or ads 370.

Although performance is improved when an index is used, such an index isnot required. For example, features (and perhaps weights) for a documentmay be derived directly from query (and perhaps user actions) associatedwith the document. FIG. 4 is a bubble diagram illustrating operationsthat may be performed, and information that may be generated and/orstored, by document feature generation and/or update system consistentwith the present invention. Document feature generation/updateoperations 420 may use query (and perhaps user action) information todocument associations 410 to generate or update features (and perhapsweights) associated with document identifiers 430. Although notnecessary, indexing operations 440 may use this information 430 togenerate an index of document identifiers to (weighted) featuresassociation information 450.

§ 4.3.2 Exemplary Methods

FIG. 5 is a flow diagram of an exemplary method 500 that may be used togenerate and/or update document feature information in a mannerconsistent with the present invention. A document identifier (e.g., aURL of a Web page) is accepted (Block 510) and query information (and/oruser action information) associated with the identified document isobtained (Block 520). As indicated by bracket 230′, blocks 510 and 520exemplify a method, consistent with the present invention, which may beused to perform the document query information lookup operations 230 ofFIG. 2. Then, features and/or weights are generated using the obtainedquery information (and/or user action information) (Block 530), and thefeatures, perhaps weighted features, are stored in association with thedocument (Block 540) before the method 500 is left (Node 550). Asindicated by bracket 250′, blocks 530 and 540 exemplify a method,consistent with the present invention, which may be used to perform thedocument feature generation/update operations 250 of FIG. 2.

FIG. 6 is a flow diagram of an exemplary method 600 that may be used togenerate and/or update document feature information in a mannerconsistent with the present invention. Query (and perhaps user action)information for a document is accepted. (Block 610) If any (weighted)feature information already exists for the document, it may be accepted.(Block 620) For example, the method 600 may be used to update alreadyexisting document (weighted) feature information. New (weighted) featureinformation is then determined for the document, or existing (weighted)feature information for the document is updated. (Block 630) Thedetermined and/or updated (weighted) features are then stored inassociation with the document (Block 640) before the method 600 is left(Node 650).

In one embodiment consistent with the present invention, the featuresmay be unigrams and n-grams, the document may be a Web page and thedocument identifier may be a URL of the Web page. Alternatively, or inaddition, the features may be keywords, such as keywords used fortargeting ads for example. Alternatively, or in addition, the featuresmay be concepts, such as concepts used for targeting ads for example.The features may have associated weights in which higher weightsindicate features more closely associated with the Web page. Thus, theWeb page may have an associated weighted feature vector generated and/orupdated by embodiments consistent with the present invention.

Methods consistent with the present invention, such as the methods 500and 600 may be performed for a number of Web pages. Thus, the methods500 and 600 may be performed for each URL u in plurality of URLs. In anexemplary embodiment, a plurality of queries Q are retrieved from aplurality of logged queries that returned the URL in a list of searchresults. (Note that if the document is an ad, or a Web page linked froman ad, the plurality of queries Q may be retrieved from a plurality oflogged queries that returned the ad in a set of one or more ads renderedwith on the search results page. Features from the queries may be usedto populate (and/or update weights of) a feature vector associated withthe URL. In one embodiment, only information from queries under which aURL selection occurred is used to populate (and/or update weights of) afeature vector associated with the URL. In yet another embodiment,information from all queries that returned the URL in a list of searchresults is used to populate (and/or update weights of) a feature vectorassociated with the URL, but a user action is used to weight thefeatures. For example, information from a query that led to a selectedURL may be weighted more than information from a query that let to arendered URL that was not selected. Other user actions may also affectthe feature weight. For example, the feature may be weighted more if along dwell time occurred after selection than if a short dwell timeoccurred after selection. As another example, the feature may be weighedmore if a conversion occurred after selection of a URL than if noconversion occurred after selection of a URL.

Different embodiments may select different features associated with theappropriate queries. For example, one embodiment consistent with thepresent invention may use all exact queries as associated features. Asanother example, another embodiment consistent with the presentinvention may use all n-grams from length I_1 to length I_2 asassociated features (optionally with “stop” words and/or non-contentwords such as “the” removed). In many cases, there will be a set offeatures that “best” specify a document. If the features are scored andweighed such that the sum of the weights equals 1.00, one embodimentconsistent with the present invention would be to take the features withthe best weights until the sum of factors reaches some value (e.g.,0.80). In an alternative embodiment consistent with the presentinvention, features with weights less than a predetermine percent (e.g.,20%) of the weight of the best feature could be ignored. Still otherembodiments consistent with the present invention ma use somecombination of the forgoing concepts (e.g., filtering features usingabsolute and/or relative weight or score thresholds) to obtain the“best” set features for a given document, or to filter out featureswithout a strong affinity to the document.

The (e.g., weighted) features associated with a document may be used ina variety of ways. For example, the features may be used as documentrelevance information when determining a match (e.g., a similarity) toan ad in a content-relevant ad server such as the one described in the'900 patent application. As another example, the features may be used toprovide or suggest keywords (e.g., used for an ad where the ad is thedocument, or wherein a landing page of the ad is the document).

§ 4.3.3 Exemplary Apparatus

FIG. 7 is high-level block diagram of a machine 700 that may perform oneor more of the operations discussed above. One or more such machines 700may be used as a content-relevant ad server, a separate server, clientdevices, etc. The machine 700 basically includes one or more processors710, one or more input/output interface units 730, one or more storagedevices 720, and one or more system buses and/or networks 740 forfacilitating the communication of information among the coupledelements. One or more input devices 732 and one or more output devices734 may be coupled with the one or more input/output interfaces 730.

The one or more processors 710 may execute machine-executableinstructions (e.g., C or C++running on the Solaris operating systemavailable from Sun Microsystems Inc. of Palo Alto, Calif. or the Linuxoperating system widely available from a number of vendors such as RedHat, Inc. of Durham, N.C.) to effect one or more aspects of the presentinvention. At least a portion of the machine executable instructions maybe stored (temporarily or more permanently) on the one or more storagedevices 720 and/or may be received from an external source via one ormore input interface units 730.

In one embodiment, the machine 700 may be one or more conventionalpersonal computers. In this case, the processing units 710 may be one ormore microprocessors. The bus 740 may include a system bus. The storagedevices 720 may include system memory, such as read only memory (ROM)and/or random access memory (RAM). The storage devices 720 may alsoinclude a hard disk drive for reading from and writing to a hard disk, amagnetic disk drive for reading from or writing to a (e.g., removable)magnetic disk, and an optical disk drive for reading from or writing toa removable (magneto-) optical disk such as a compact disk or other(magneto-) optical media.

A user may enter commands and information into the personal computerthrough input devices 732, such as a keyboard and pointing device (e.g.,a mouse) for example. Other input devices such as a microphone, ajoystick, a game pad, a satellite dish, a scanner, or the like, may also(or alternatively) be included. These and other input devices are oftenconnected to the processing unit(s) 710 through an appropriate interface730 coupled to the system bus 740. The output devices 734 may include amonitor or other type of display device, which may also be connected tothe system bus 740 via an appropriate interface. In addition to (orinstead of) the monitor, the personal computer may include other(peripheral) output devices (not shown), such as speakers and printersfor example.

Each of the ad server 110, the search engine 120, the content server130, the e-mail server 140, and the user device 150, etc., may beembodied by one or more such machines 700.

§ 4.3.4 Refinements and Alternatives

Although the method 600 of FIG. 6 was described in the context ofdetermining and/or updating (e.g., the weight of) unigram or n-gram todocument associations, embodiments consistent with the present inventionmay be used to determine and/or update (the weight of) otherfeature-to-entity associations (e.g., keyword-to-category associations,category-to-ad associations, etc.). First, a feature-to-entityassociation is accepted or generated. Then, the association is used togenerate (e.g., a document with) results. For example,keyword-to-category associations may be used to determine a Web pagewith selectable category listings in response to a query including thekeyword. As another example, category-to-ad associations may be used todetermine a Web page including one or more ads when a category isselected (or if the Web page has content that pertains to the category).User behavior with respect to the results (e.g., selection or not,conversion or not, dwell time, etc.) may be tracked. The tracked userbehavior may then be used to update (e.g., the weight of, generallyreferred to as the “score” of) the feature-to-entity association.

Thus, suppose for example that three keyword-to-category associationswere used to generate a Webpage with a three selectable category links.Suppose further that the user selected the first category link butquickly returned. Now suppose that the user selected the second categorylink and dwelled on the linked page. Finally, suppose that the user didnot select the third category link. The keyword-to-first categoryassociation may be somewhat strengthened (e.g., due to the userselection), but not too much (e.g., due to the short dwell time andquick return), the keyword-to-second category association may bestrengthened to a greater degree (e.g., due to the user selection andlong dwell time), and the keyword-to-third category association may beweakened (e.g., due to the fact that the user did not select the thirdcategory link).

Refinements of, and alternatives to, the embodiments described above arepossible. Each of the features may be given a score. The score may beused to determine a weight to assign to the feature, and/or to filterfeatures. For example, a feature with a higher score may receive ahigher weight, while a feature with a lower score may receive a lowerweight. Weight should be a monotonic function of score, but need not belinear. The score may also be compared with a given (e.g.,predetermined) threshold. If the score for the feature is below thethreshold, the feature may be removed from association with thedocument, or it may be weighted to zero. The threshold may be absolute,and/or relative. For example, an absolute threshold might filter out afeature if its score did not exceed a predetermined value, while arelative threshold might filter out a feature that was not one of thetop twenty features for the document.

The score may be a function of one or more of (a) a frequency of thefeature with respect to the document, (b) a user action with respect tothe document, (c) feature scores of related or similar documents, (d)total frequency and inverse document frequency of the feature, (e)general performance (e.g., selection rate, conversion rate, etc. acrossall queries) of the document, etc. Examples of each of these factors aredescribed below.

Frequency

The feature score may be a function of the frequency of the feature(e.g., generated from query information). More frequent features may begiven a higher score for example. The feature score may also be afunction of the frequency of selections (e.g., clickthroughs) and/orqueries for that term.

User Action

The feature score may be a function of a user action with respect to thedocument. For example, if the user selected the document when it wasrendered on a search results page to a query, features from the querywould be scored higher than if the document were not selected. Asanother example, if the user competed a transaction at a document whenit was rendered on a search results page to a query, features from thequery would be scored higher than if the no conversion took place on thedocument. Dwell time may also be considered. For example, if the userselects and dwells on the document for a long period of time when it wasrendered on a search results page to a query, features from the querywould be scored higher than if the document were selected but the useronly dwelled on the document for short period of time. Indeed, a veryshort dwell time may be used to discount a score enhanced by the factthat a user selected the document.

Feature Scores of Related or Similar Documents

Since there may be few queries and/or user actions (e.g., selections,conversions, etc.) for some documents, it may be desirable to groupdocuments together and treat them collectively, applying features andweights or scores across more than one document of the group. Documentsmay be grouped with other documents in various ways. For example, forWeb page documents, it may be desirable to combine the analysis formultiple URLs on a Website, for URLs within a directory, URLs on similartopics, linked documents, etc. As a more specific example, all URLs on aWebsite may be grouped together, and all queries (and user actions) thatlead to the Website are used to find features for Web pages of theentire Website. Similar pages may be computed using, for example,TF-IDF.

Consider URL u, a set of other URLs within the same directory of theWebsite S_1, a set of all URLs on the same Website S_2, and a set of allURLs with similar content S_3. Consider n-gram features T within queriesthat resulted in a clickthrough event to the URL u. A score S_t can beassigned for each term t in T, for example, as follows:S _(—) t=w _(—)1*f(S _(—)1)+w _(—)2*f(S _(—)2)+w _(—)3*f(S _(—)3)+w_(—)4*f(u)where f(S) is a function of the queries and user actions correspondingto URLs within set S. For example, as above, f(S) may factor in thenumber of occurrences of term t, user selections, and dwell times on theURL or site that the clicked through to. Weights w_1 to w_4 allow thecontribution of each set to vary.

Another improvement is possible by considering the probability of a useraction (e.g., selection) for a URL for a term or query. In this case,the expected user action (e.g., selection) can be compared based on theposition of a URL in the result list, with the actual user action (e.g.,selection). Features may be weighted according to their user action(e.g., selection) rate, with features that result in user action ratesabove the average (expected) rate being given higher weights, andfeatures that result in user action rates below the average (expected)given lower weights.

Levels of Tracking

The features and/or feature scores associated with a document may betracked generally, over all users, or may be tracked per user group, orper individual user. That is, it may be desirable to segment the queryand user action data for different types of users in order to createdifferent sets of associated features that may subsequently be used withthe different types of users. For example, information may be trackedand aggregated per user group (e.g., users within differentdemographics, users with similar interests, or individual users). Forexample, a separation by age groups may result in different featuresbeing the best associated features for a specific document. Similarly,if detailed information is available for the interests of a user, theassociated features may be biased toward the interests of that user, forexample by increasing the weight of features in the analysis aboveaccording to the weight of those features for the interests of the user.

Data Structures

Referring back to FIG. 2, different information associations 210 may bestored and/or accessed, depending on the particular embodiment used. Forexample, the information associations 210 may include one or more of (i)whether or not the document was selected, (ii) qualitative orquantitative dwell time information, (iii) query frequency, (iv) queryparts, (v) document site information, (vi) document directoryinformation, (vii) document group information, (viii) user information,etc.

Features

Instead of, or in addition to, search query information corresponding toa document, other serving parameters, such as those listed in § 4.1.above for example, may be used to assign and/or weight features.

§ 4.4 Operational Example of an Exemplary Embodiment

FIG. 8 is a diagram illustrating an example of how an exemplaryembodiment consistent with the present invention can be used toassociate features (such as terms, n-grams, etc.) with entities (such ascategories). As shown, in this exemplary embodiment, a query processor820 returns a document 830 in response to received query information810. The query information 810 may include search query terms. Thedocument may include one or more of (a) search results 832 includinglinks to documents 840, (b) keyword targeted and/or category targetedads 834 including links to ad landing pages 850, and (c) category links836 to pages 860 including category targeted (which may also be keywordtargeted) ads. The document 830 may include other links to other typesof information as well.

Upon end user selection of one of the search result links 832, acorresponding document 840 is returned (e.g., loaded into a browser ofan end user device). Upon end user selection of one of the ad links 834,a corresponding ad landing page 850 is returned. Finally, upon end userselection of one of the category links 836, a corresponding pageincluding one or more category targeted ads 860 is returned. One or moreads with links to ad landing pages may also be provided, for example,below associated category headings or links. If the end user selects oneof the ads on document 860, a corresponding ad landing page 850 isreturned.

In at least some alternative embodiments consistent with the presentinvention, if an end user selects one of the category links 836, a“filtered” version of the document 830 may be rendered. In such a“filtered” version of the document 830, search results 832, keywordand/or category targeted ads 834, and/or category links 836 may befiltered such that they pertain to the selected category.

In the case where search results 832 are returned, embodimentsconsistent with the present invention may be used to associate queryinformation 810 with the listed documents, and/or any selecteddocument(s) 840. Such an association may reflect whether or not adocument was selected.

In the case where keyword targeted and/or category targeted ads 834 arereturned, embodiments consistent with the present invention may be usedto associate query information 810 with listed ads, and/or any selectedad(s) 850. Such an association may reflect whether or not an ad wasselected. Further, the present invention may be used to associate queryinformation 810 with keywords and/or concepts used to target the servingof the ads 834. Such an association may reflect whether or not an ad wasselected.

In the case where category links 836 are returned, embodimentsconsistent with the present invention may be used to associate queryinformation 810 with listed categories and/or any selectedcategory(ies). Such an association may reflect whether or not a categorywas selected. Alternatively, or in addition, such an association mayreflect whether or not a category targeted ad on page 860 was selected.Further, the present invention may be used to associate queryinformation 810 with keywords and/or concepts used to target the servingof the ads on page 860.

An embodiment in which the document 830 includes category links 836 to apage 860 with one or more category targeted ads may be used, forexample, to provide “Yellow Pages” style classification to ads, such aslocal ads for example. As a more specific example, suppose that an adserving system includes the category “plumbers,” and one or moreadvertisers associate their ad campaigns with this “Yellow Page”category.

Suppose further that when an end user enters the query 810 “cloggeddrain,” category links 836 include a “Local Plumbers” category link.(This keyword to category association may have been derived from thefact that one or more advertisers associated both the keyword target“clogged drain” and the category “Plumbers” with their ads.Alternatively, or in addition, a category may be inferred from acollection of words (e.g., extracted from ad information).)

If the end user then selects the “Local Plumbers” category link, theyare provided with a page 860 containing one or more ads from localplumber advertisers. Embodiments consistent with the present inventionmay create an association, or reinforce an existing association, betweenthe feature “clogged drain” and the entity “category=Plumbers.”

Now suppose that an end user enters the query 810 “DIY clogged drain”and that a document 830 with category links 836 including the “LocalPlumbers” category link is provided. However, suppose that the end userdoes not select the “Local Plumbers” category link becausedo-it-yourselfers won't usually hire a plumber. Suppose instead that theuser selects a “Local Plumbing Supplies” category link 836. Lack ofselections (or short dwell times) of the “Local Plumbers” category linkindicates an negative correlation between the query information “DIYclogged drain” and the “Local Plumbers” category, while selections (orlong dwell times) of the “Local Plumbing Supplies” category indicates ancorrelation between the query information “DIY clogged drain” and the“Local Plumbing Supplies” category.

In at least some embodiments consistent with the present invention, whenthe “Local Plumbers” category link 836 is selected, a page 860 withlocal plumber ads (which may also be targeted by keywords carriedthrough from the terms of the search query 810) is provided. If the page860 also includes ads having a strong association to a category (e.g.,due to advertiser association), then a similar process, in which it isdetermined just how strong the association between the advertiser andthe category is by observing action or inaction on that advertiser'slink, may occur. That is, an ad-category association may be modifieddepending on a user action with respect to the ad when the category wasused to target the serving of the ad(s) on the page 860 (and possiblymodified by keywords carried through from the original query 810).

As such information is gathered and analyzed, a strong affinity between“clogged drain” and the “Yellow Pages” category “Plumbers” (as long asthe term “diy” is not included) is learned.

The fact that some advertisers who indicate that they are “plumbers”(e.g., by associating their ad with the category plumbers) may have adsthat aren't selected much (or dwelled on) may be learned. Using suchinformation, an ad serving system may cease to provide such ads in apage 860 linked from the category link 836 “Local Plumbers”.Alternatively, in an ad serving system in which ads are scored, thescores of such ads may be reduced.

Finally, for ads without an associated category (and even for ads withan associated category), if there is a strong association (e.g.,correlation) between such ads and one or more categories, at least someembodiments consistent with the present invention may be used torecommend to advertisers that they associate their ad with suchcategories. For example, such an embodiment may recommend that anadvertiser with an ad with the targeting keywords “clogged drain” and“emergency service” associate its ad with the category “Plumber”.Alternatively, such an association may be generated automatically.

§ 4.5 Conclusions

As can be appreciated by the foregoing, embodiments consistent with thepresent invention may be used to assign and/or weight features, such asn-grams, to entities, such as documents or concepts. The assignedfeatures may represent relevance of the document and may be used totarget the serving of advertisements with the document.

1. A computer-implemented method comprising: a) obtaining servinginformation related to a document; b) determining features using theobtained serving information; and c) associating the features determinedwith the document.
 2. The computer-implemented method of claim 1 furthercomprising: d) determining whether or not to serve an ad with thedocument using the features associated with the document.
 3. Thecomputer-implemented method of claim 1 wherein the serving informationrelated to the document includes information from at least one pastquery that caused the rendering of information of the document on asearch results list.
 4. The computer-implemented method of claim 3wherein the serving information related to the document includes whetheror not the rendered information of the document was selected.
 5. Thecomputer-implemented method of claim 3 wherein the serving informationrelated to the document includes a time that the user dwelled on thedocument after selecting the rendered information of the document. 6.The computer-implemented method of claim 3 wherein the document is a Webpage.
 7. The computer-implemented method of claim 6 wherein the servinginformation related to the document includes information from at leastone past query that caused the rendering of information of the documenton a search results list.
 8. The computer-implemented method of claim 6wherein the serving information related to the document includes whetheror not the rendered information of the document was selected.
 9. Thecomputer-implemented method of claim 6 wherein the serving informationrelated to the document includes a time that the user dwelled on thedocument after selecting the rendered information of the document. 10.The computer-implemented method of claim 1 further comprising: e)obtaining user action information related to the document using thedocument identifier; f) determining scores using the user actioninformation; and g) assigning weights to the features using the scoresdetermined.
 11. The computer-implemented method of claim 10 wherein eachof the weights is a monotonic function of an associated one of thescores.
 12. The computer-implemented method of claim 11 wherein the useraction is a dwell time after a selection, and wherein the score ishigher for a longer dwell time than for a shorter dwell time.
 13. Thecomputer-implemented method of claim 11 wherein the user action isselection, and wherein the score is higher for a selection than for anon-selection.
 14. The computer-implemented method of claim 11 whereinthe user action is conversion, and wherein the score is higher for aconversion than for a non-conversion.
 15. The computer-implementedmethod of claim 1 further comprising: e) determining scores using theserving information ; and f) assigning weights to the features using thescores determined.
 16. The computer-implemented method of claim 15wherein the score for a feature is determined using a frequency of thefeature in the serving information .
 17. The computer-implemented methodof claim 15 wherein the score for a feature is determined using aninverse frequency of the feature in serving information for a collectionof documents.
 18. The computer-implemented method of claim 1 furthercomprising: e) obtaining user action information related to the documentusing the document identifier; f) determining scores using both theserving information and the user action information; and g) assigningweights to the features using the scores determined.
 19. Thecomputer-implemented method of claim 18 wherein each of the weights is amonotonic function of an associated one of the scores.
 20. Thecomputer-implemented method of claim 1 further comprising: e)determining scores using at least one of (A) the serving information and(B) user action information related to the document; and f) filteringthe features using the scores determined.
 21. The computer-implementedmethod of claim 20 further comprising: g) assigning weights to thefeatures using the scores determined.
 22. The computer-implementedmethod of claim 1 wherein at least one of the features is an n-gram. 23.The computer-implemented method of claim 1 wherein at least one of thefeatures is a keyword.
 24. The computer-implemented method of claim 1wherein at least one of the features is a concept.
 25. Thecomputer-implemented method of claim 1 wherein the serving informationrelated to the document is obtained using an accepted documentidentifier.
 26. The computer-implemented method of claim 25 wherein thedocument identifier is a universal resource locator.
 27. Acomputer-implemented method comprising: a) accepting a feature-to-entityassociation; b) using the feature-to-entity association to generate oneor more results for presentation to a user; c) tracking user behaviorwith respect to the results; and d) updating a score associated with thefeature-to-entity association using the tracked user behavior.
 28. Thecomputer-implemented method of claim 27 wherein the feature-to-entityassociation is a keyword-to-category association.
 29. Thecomputer-implemented method of claim 28 wherein the one or more resultsgenerated include one or more category listings provided on a document.30. The computer-implemented method of claim 27 wherein thefeature-to-entity association is a category-to-ad association.
 31. Thecomputer-implemented method of claim 30 wherein the one or more resultsgenerated include one or more category targeted ads provided on adocument.
 32. The computer-implemented method of claim 27 wherein theuser behavior includes whether or not a user selects a result.
 33. Thecomputer-implemented method of claim 27 wherein the user behaviorincludes whether or not a user converts on a result.
 34. Apparatuscomprising: a) means for obtaining serving information related to adocument; b) means for determining features using the obtained servinginformation; and c) means for associating the features determined withthe document.
 35. Apparatus comprising: a) means for accepting afeature-to-entity association; b) means for using the feature-to-entityassociation to generate one or more results for presentation to a user;c) means for tracking user behavior with respect to the results; and d)means for updating a score associated with the feature-to-entityassociation using the tracked user behavior.